How do you take an analog system and fully automate it using AI agents?
Generative AI Lab, or GenAI-Lab, pairs MIT student teams with companies across the globe to help solve such challenges. Throughout the semester, students design and build practical AI solutions and uncover new AI-driven opportunities. Run by MIT Sloan and one of two Action Learning courses conducted in partnership with the MIT Initiative on the Digital Economy, GenAI-Lab focuses on strategic implementation and meaningful change. It also uses a healthy dose of competition to motivate teams, awarding the best project with a trophy and all the top projects with bragging rights.
The student team runner-up this past spring, Team iTradeNetwork, was challenged with creating a digital transformation and AI implementation road map for a market that is fully analog. The result: a ready-to-deploy marketplace, powered by AI agents, where food can be sold in seconds instead of days.
Here’s how the four builders—MIT students Ido Levy, Cesar Dori, Pat Ovando Roche, and Elie Juvenspan—created their project.
A $166 Billion Market Running on Phone Tag
Typically food in the U.S. is sold through pre-arranged agreements between farmers and retailers. But roughly one-third of the perishable food grown in the U.S. is sold on the spot market, worth roughly $166 billion a year.
This spot market is challenging for both farmers and buyers. Produce has a short shelf life. After just two to three weeks, a box of strawberries or head of lettuce will spoil. And half of that shelf life is routinely taken up by the negotiation process, costing billions of dollars in lost product across the marketplace every year.
The problem’s source is the analog system farmers and buyers use on the spot market. Closing a single spot deal requires a grower to run through a sequential gauntlet: First identify potential buyers, then reach them by phone to negotiate prices and quantities. Next, they align on delivery timing and logistics and finally generate the paperwork. What’s more, all of this must be done while managing other open inventory.
Even when the work goes smoothly, there’s another challenge. By the time a price is agreed, a delivery window confirmed, and a purchase order generated, the produce is days closer to spoiling.
One company hoping to solve this problem is iTradeNetwork. It has spent the last 26 years building a digital backbone for the B2B perishable food trade. The company’s platform serves major buyers and sellers. Even on iTrade’s platform, the spot market remained stubbornly manual. There was no mechanism to quickly match supply and demand, let alone negotiate deals. Every transaction still relied on human brokers working sequentially, one call at a time.
Starting With the People, Not the Technology
The team’s first move was to talk to the people in the food industry. They interviewed both buyers and sellers actively trading on the iTradeNetwork platform to learn about their daily workflow, sources of friction, and suggests for improvements.
“When we talked to iTrade and other companies, the C-suite has some idea of what they want to build,” said Ido Levy, MIT Sloan Fellow and team member. “But then you talk to the people actually doing the work, and it’s a big jump from how it’s being done now to an AI marketplace. I needed to hear what the market actually needs.”
The team heard from industry members that closing a deal is the biggest bottleneck. These conversations also clarified the opportunity’s scale. With one-third of perishable food moving through the spot market, that’s where growers and buyers are most exposed—and most underserved by technology.
The Agent-to-Agent Platform for the Spot Market
Working with this information, the team decided that farmers and buyers needed a digital marketplace to come together and begin negotiations. The resulting platform is called NegotiateAI, an agent-to-agent marketplace that operates across four layers. Here’s how it works:
The marketplace: Sellers use the marketplace to post inventory, while buyers post demand. A matching algorithm identifies the zone of possible agreement (ZOPA) by evaluating overlap across five dimensions: price, quantity, timing, location, and quality. Once the algorithm identifies that a deal could exist, a negotiation room opens in milliseconds. But if there’s no ZOPA, no room opens, and no one’s time is wasted.
The negotiation swarm: Each company’s product gets its own AI agent with a distinct negotiation style—balanced, aggressive, cautious, or collaborative. Products are assigned hard-coded guardrails, and a company-defined prompt that shapes how the agent communicates and reasons with its counterpart.
A negotiation starts when a seller agent makes a proposal. Then buyer agents can offer counterproposals. The two agents negotiate until they either reach an agreement or walk away.
The Orchestrator: Once two agents reach a deal, a master-agent called the Orchestrator calculates the expected value of the agreement and compares it against with other open negotiation rooms in the swarm. The Orchestrator predicts and selects the highest-value deal. If a competing room has an 80% probability of closing at better terms, the Orchestrator may hold. Finally, the Orchestrator escalates the deal to a human supervisor for final approval.
Human approval. A human supervisor reviews the deal’s terms along with the negotiation’s history and the Orchestrator’s reasoning. The supervisor then approves, modifies or rejects the deal. These decisions are recorded so that the next negotiation starts smarter.
“Anyone can build a chatbot that haggles,” Levy said. “The real breakthrough was the Orchestrator, an AI that doesn’t just close the best deal in the room but also looks across every open negotiation simultaneously and decides which deal is worth closing. That’s where the system stops being a tool and starts behaving like a trader.”
The Results
The agents’ negotiation behavior was grounded in 182,812 simulations, giving the system a foundation built on real outcome data rather than assumptions.
Across those trials, the system reached agreement more than 90% of the time. Walk-away rates held at just 5%. That’s well below the rates seen in human negotiations, where emotion, fatigue, and sequential bottlenecks can erode outcomes.
The Orchestrator then approved 56% of completed agreements. It did so after running expected-value calculations across the full swarm, then selecting the best available deal rather than just the first one.
The entire process—match, negotiate, orchestrate, escalate—runs in just 22 seconds. What previously took days of phone calls, voicemails, and manual back-and-forth now closes before a grower finishes their morning coffee.
“The hard part was getting a swarm of hundreds of agents to negotiate simultaneously, each reasoning through real constraints, modeling counterpart behavior, and converging on the optimal decision,” Levy said. “All in real time, at scale, while producing outcomes that made economic sense for both buyers and sellers. We may have just decoded what the future of commerce looks like.”
What’s Next for the NegotiateAI Platform?
NegotiateAI is a working production model, and it’s ready for iTradeNetwork to begin the work of bringing buyers and sellers onto the platform. The system is also designed to be additive, not disruptive: Buyers and sellers can continue making calls while trying out the platform. This lowers the barrier to adoption in an industry that for decades has resisted change.
“Our view is that GenAI creates value when it is paired with real domain expertise and disciplined execution, and this work proved it,” said Sid Dixit, Chief Technology Officer at iTradeNetwork. “They landed on something deployable, closing in seconds what used to take days, with a human in control of every decision.”
The next step, Dixit adds, will be a controlled pilot with live suppliers and buyers, empowering the marketplace to have a real impact on the food supply chain.
A Team Motivated to Deliver
Levy said much of the success of the project came from iTrade’s transparency and willingness to help the Action Lab team build something deployable.
The company-side commitment mattered, too. iTrade wasn’t just a client with a brief, but a partner invested in seeing the work move toward production. That dynamic, Levy notes, is part of what makes GenAI-Lab different from a case study or a capstone project.
“I came to MIT to build something that didn’t exist yet,” Levy said. “Agent-to-agent negotiation at production scale was an unsolved problem. MIT gave us the stage, the partners, and the mandate to bring it to life. We didn’t just build a product for food. We built the architecture for how agents will trade.”